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coordinationcoordination 搜索

Agent Skill

coordination 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

593

周安装

24

GitHub Stars

222

下载量

186
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:coordination(coordination 搜索)
来源仓库:https://github.com/transilienceai/communitytools
仓库路径:skills/coordination
安装命令:
npx skills add https://github.com/transilienceai/communitytools --skill coordination
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/transilienceai/communitytools --skill coordination

简介

用于查找、检索和筛选相关信息。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

  • 适合根据关键词、任务场景或来源线索快速定位候选结果。
  • 可结合来源仓库和原始 README 核验具体用法。
  • 安装前建议确认权限范围、维护状态及是否会触发联网或文件读写。
  • coordination 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Coordination

Inline. Holds context. Thinks before every action.

Workflow

P0: Ingest scope
 ↓
P1: Recon + read source code → write attack-chain.md + create experiments.md header
 ↓
┌→ P2: Think — read chain + experiments.md, dedup, design 1-2 experiments
│  P2b: Research (conditional) — see reference/creative-research.md
│  P3: Execute — spawn 1-2 executors with CHAIN_CONTEXT [+ RESEARCH_BRIEF]
│  P4: Integrate — read results, update chain, revise theory
│      No progress 2 batches → P4b
│      Goal → P5
└─ loop (max 30 experiments)

P4b: Reset — re-read all recon + source + chain. Creative Research (MANDATORY). Fresh theory.
P5: Validate + Report

Steps

  1. Recon + Source Code — read all accessible source code (see formats/reconnaissance.md). Create {OUTPUT_DIR}/experiments.md with header row (see format below).
  2. Think — write theory + next step to attack-chain.md
  3. Test — 1-2 executors per batch, integrate before next
  4. Validate — validators per-finding (see skills/coordination/reference/VALIDATION.md)
  5. Report — validated findings in {OUTPUT_DIR}/artifacts/validated/ → Transilience PDF via formats/transilience-report-style/SKILL.md (MANDATORY)

attack-chain.md

At {OUTPUT_DIR}/attack-chain.md. Updated every batch. Sections: services, surface, theory, tested, next.

Keep it terse — bullet points, no prose.

experiments.md

At {OUTPUT_DIR}/experiments.md. Append-only table — never prune, never rewrite. Format: formats/logs.md.

  • P1: create header. P2: read → dedup → append row (result=pending). Executor updates on completion.
  • Dedup: skip if same technique + target exists unless parameters differ meaningfully.
  • 3-strike: count(technique, result=fail) >= 3 → triggers rule 12.

tools/

Executors log every significant tool invocation to {OUTPUT_DIR}/tools/{NNN}_{tool}.md with input + output. See formats/logs.md.

Creative Research (P2b)

Triggers — research when ANY of:

  • P4b reset (mandatory)
  • 3-strike stuck detection fires (rule 12)
  • New tech/framework discovered not in mounted skills
  • No clear hypothesis at P2

Method: follow reference/creative-research.md. Synthesize model knowledge + online sources + skill cross-reference into a RESEARCH_BRIEF (max 10 lines) appended to executor prompt.

Do NOT research every batch. Most batches skip P2b entirely.

Spawning

Consult reference/context-injection.md before building any agent prompt.

executor = Read("skills/coordination/reference/executor-role.md")
chain = Read(f"{output_dir}/attack-chain.md")
experiments = Read(f"{output_dir}/experiments.md")

# Optional: if P2b produced a brief
# research = "RESEARCH_BRIEF:\n- [model] ...\n- [web] ...\n- [skills] ..."

# 1-2 executors per batch — pass only relevant PATT_URL, not full map
Agent(prompt=f"{executor}\nMISSION_ID: m-001\nEXPERIMENT_ID: E-001\n"
      f"CHAIN_CONTEXT: {chain}\nEXPERIMENTS: {experiments}\n"
      f"OBJECTIVE: ...\nSKILL_FILES: ...\nPATT_URL: ...\nOUTPUT_DIR: {output_dir}\n"
      f"{research if research else ''}",
      description="Blind SQLi /search", run_in_background=True)

# Wait. Read results. Think. Update attack-chain.md. THEN next batch.

# Validators — one per finding (BLIND REVIEW — see context-injection.md)
validator = Read("skills/coordination/reference/validator-role.md")
Agent(prompt=f"{validator}\nfinding_id: F-001\n"
      f"FINDING_DIR: {output_dir}/findings/finding-001/\n"
      f"TARGET_URL: ...\nOUTPUT_DIR: {output_dir}/artifacts",
      run_in_background=True)

# After all validators complete:
# 1. Read artifacts/validated/ and artifacts/false-positives/
# 2. Verify each validated finding has findings/{id}/evidence/validation/validation-summary.md
# 3. Flag any finding that passed validation but has no proof

Pass only the relevant PATT_URL for this mission, not the full URL map.

Roles

RoleFileContext
Executorreference/executor-role.mdFull chain + skills
Validatorreference/validator-role.mdEvidence only (blind)

See reference/context-injection.md for what each role receives and what is withheld.

Rules

  1. Autonomous. Never ask user.
  2. Think before acting. Write reasoning to attack-chain.md before every batch.
  3. Max 1-2 executors per batch. Recon can use more.
  4. Source code first. Understanding beats guessing.
  5. Pass chain context + specific PATT_URL to executors.
  6. 30 experiment cap.
  7. Stuck 2 batches → re-read everything, fresh theory.
  8. All output to OUTPUT_DIR.
  9. Report gate: validated findings exist → PDF report required. Read formats/transilience-report-style/pentest-report.md.
  10. After validators complete, verify each validated finding has evidence/validation/validation-summary.md. Flag any that passed without proof.
  11. Sequential flag progression. In multi-flag challenges (HTB machines), secure each flag before attempting the next. The user-flag path often provides the foothold needed for root.
  12. 3-strike stuck detection. If experiments.md shows >= 3 fail rows for the same technique, STOP. Write to attack-chain.md: (a) why it's failing, (b) is this path fundamentally blocked, (c) alternative paths. Do NOT continue retrying.
  13. Read before calling library internals. Before writing Python against any library's internal API (Impacket, ldap3, pyasn1), read the relevant source file first. Never guess function signatures. Prefer CLI tools (secretsdump.py, ticketer.py, getST.py) over raw API calls.
  14. Background command discipline. Before spawning a background command, state what specific result it will produce. No speculative tunnels, relays, or listeners without a concrete plan to use them.
  15. Creative Research triggers: P4b (mandatory), 3-strike stuck, new tech discovered, no hypothesis. Follow reference/creative-research.md. Max 3 WebSearch + 2 WebFetch per cycle.

Token Discipline

  • Internal output (chain, logs, reports): terse. Bullets, not paragraphs.
  • Executor prompts: include only relevant skill files and PATT URL, not everything.
  • Don't inject patt-fetcher/SKILL.md into executor prompts. Pass only the relevant PATT_URL.
  • Don't inject skill files the executor won't use. Pick the 1-2 most relevant.
  • attack-chain.md: max 50 lines. Prune old tested items to one-liners.
  • User-facing output (reports, summaries): detailed and professional.

References

reference/ATTACK_INDEX.md · reference/OUTPUT_STRUCTURE.md · reference/VALIDATION.md · reference/GIT_CONVENTIONS.md · reference/context-injection.md · reference/creative-research.md · formats/INDEX.md

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.52%
按下载量换算64

Claude

30.88%
按下载量换算57

Cursor

16.6%
按下载量换算31

Gemini CLI

9.04%
按下载量换算17

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/transilienceai/communitytools --skill coordination 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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